






'''simple docstring'''


import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_					   =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					   =				[
    ["""attention""", """attn"""],
    ["""encoder_attention""", """encoder_attn"""],
    ["""q_lin""", """q_proj"""],
    ["""k_lin""", """k_proj"""],
    ["""v_lin""", """v_proj"""],
    ["""out_lin""", """out_proj"""],
    ["""norm_embeddings""", """layernorm_embedding"""],
    ["""position_embeddings""", """embed_positions"""],
    ["""embeddings""", """embed_tokens"""],
    ["""ffn.lin""", """fc"""],
]






def 	lowerCAmelCase__      (       SCREAMING_SNAKE_CASE__		):
 if k == "embeddings.weight":
  return "shared.weight"

 for parlai_name, hf_name in PATTERNS:
  __a     :						Dict          =    k.replace(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__		)

 if k.startswith('encoder'		):
  __a     :						List[Any]          =    k.replace('.attn'							,		'.self_attn'		)
  __a     :						str          =    k.replace('norm1'							,		'self_attn_layer_norm'		)
  __a     :						List[str]          =    k.replace('norm2'							,		'final_layer_norm'		)
 elif k.startswith('decoder'		):
  __a     :						Optional[int]          =    k.replace('norm1'							,		'self_attn_layer_norm'		)
  __a     :						Union[str, Any]          =    k.replace('norm2'							,		'encoder_attn_layer_norm'		)
  __a     :						Dict          =    k.replace('norm3'							,		'final_layer_norm'		)
 return k






def 	lowerCAmelCase__      (       SCREAMING_SNAKE_CASE__		):
 __a     :						List[Any]          =    [
     """model.encoder.layernorm_embedding.weight""",
     """model.encoder.layernorm_embedding.bias""",
     """model.decoder.layernorm_embedding.weight""",
     """model.decoder.layernorm_embedding.bias""",
 ]
 for k in keys:
  __a     :						int          =    sd.pop(SCREAMING_SNAKE_CASE__		)
  __a     :						List[str]          =    k.replace('layernorm_embedding'							,		'layer_norm'		)
  assert new_k not in sd
  __a     :						Optional[Any]          =    v


SCREAMING_SNAKE_CASE_					   =				["""START"""]






@torch.no_grad()
def 	lowerCAmelCase__      (       SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__		):
 __a     :						Optional[int]          =    torch.load(SCREAMING_SNAKE_CASE__							,		map_location='cpu'		)
 __a     :						List[str]          =    model["""model"""]
 __a     :						Tuple          =    BlenderbotConfig.from_json_file(SCREAMING_SNAKE_CASE__		)
 __a     :						Union[str, Any]          =    BlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE__		)
 __a     :						str          =    m.model.state_dict().keys()
 __a     :						Any          =    []
 __a     :						List[Any]          =    {}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

  __a     :						int          =    rename_state_dict_key(SCREAMING_SNAKE_CASE__		)
  if new_k not in valid_keys:
   failures.append([k, new_k]		)
  else:
   __a     :						str          =    v
 if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
  rename_layernorm_keys(SCREAMING_SNAKE_CASE__		)
 m.model.load_state_dict(SCREAMING_SNAKE_CASE__							,		strict=SCREAMING_SNAKE_CASE__		)
 m.half()
 m.save_pretrained(SCREAMING_SNAKE_CASE__		)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_					   =				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
  parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
  parser.add_argument(
      "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
  )
  SCREAMING_SNAKE_CASE_					   =				parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch

							from transformers.generation import DisjunctiveConstraint

@require_torch
class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			def    lowerCAmelCase__						(							self			:							Dict   ):
							# For consistency across different places the DisjunctiveConstraint is called,
							# dc.token_ids is a list of integers. It is also initialized only by integers.

							UpperCamelCase_:				Optional[int]						=    [[1, 2, 4], [1, 2, 3, 4]]
							UpperCamelCase_:				List[str]						=    DisjunctiveConstraint(snake_case_   )
							self.assertTrue(isinstance(dc.token_ids    ,						snake_case_   )   )

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							# We can't have constraints that are complete subsets of another. This leads to a preverse
							# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
							# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
							# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
							# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
							UpperCamelCase_:				Dict						=    [[1, 2], [1, 2, 3, 4]]

							with self.assertRaises(snake_case_   ):
											DisjunctiveConstraint(snake_case_   )  # fails here

			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Dict						=    [[1, 2, 3], [1, 2, 4]]

							UpperCamelCase_:				Dict						=    DisjunctiveConstraint(snake_case_   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				str						=    dc.update(1   )
							UpperCamelCase_:				Union[str, Any]						=    stepped is True and completed is False and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    dc.update(2   )
							UpperCamelCase_:				Tuple						=    stepped is True and completed is False and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    dc.update(3   )
							UpperCamelCase_:				Union[str, Any]						=    stepped is True and completed is True and reset is False
							self.assertTrue(snake_case_   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 3]   )




			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

							UpperCamelCase_:				Tuple						=    DisjunctiveConstraint(snake_case_   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Dict						=    dc.update(1   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    dc.update(2   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Any						=    dc.update(4   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.current_seq == [1, 2, 4]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    dc.update(5   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

							dc.reset()

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				int						=    dc.update(1   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.remaining() == 3   )
							self.assertTrue(dc.current_seq == [1]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Tuple						=    dc.update(2   )
							self.assertTrue(not dc.completed   )
							self.assertTrue(dc.remaining() == 2   )
							self.assertTrue(dc.current_seq == [1, 2]   )

							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				List[str]						=    dc.update(5   )
							self.assertTrue(dc.completed   )  # Completed!
							self.assertTrue(dc.remaining() == 0   )
							self.assertTrue(dc.current_seq == [1, 2, 5]   )






